





import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


_lowerCamelCase   :							str    				=   logging.get_logger(__name__)


_lowerCamelCase   :							List[Any]    				=   r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""


class 		lowerCamelCase  (a_     ):







							"""simple docstring"""




							@add_start_docstrings(UpperCamelCase_		)
							def __call__(   self      :	Any,					_UpperCAmelCase      :	torch.LongTensor,					_UpperCAmelCase      :	torch.FloatTensor,					**_UpperCAmelCase      :	Tuple		) ->					Optional[Any]:





														"""simple docstring"""
														raise NotImplementedError("StoppingCriteria needs to be subclassed"		)


class 		lowerCamelCase  (a_     ):







							"""simple docstring"""




							def __init__(   self      :	Any,					_UpperCAmelCase      :	int,					_UpperCAmelCase      :	Optional[int] = None		) ->					Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__  :     Tuple           =		max_length
														SCREAMING_SNAKE_CASE__  :     Any           =		max_position_embeddings






							@add_start_docstrings(UpperCamelCase_		)
							def __call__(   self      :	int,					_UpperCAmelCase      :	torch.LongTensor,					_UpperCAmelCase      :	torch.FloatTensor,					**_UpperCAmelCase      :	Optional[Any]		) ->					Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__  :     Optional[int]           =		input_ids.shape[-1]
														SCREAMING_SNAKE_CASE__  :     List[str]           =		cur_len >= self.max_length
														if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
																					logger.warning_once(
																					    "This is a friendly reminder - the current text generation call will exceed the model\'s predefined "
																					    F'''maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '''
																					    "exceptions, performance degradation, or nothing at all."		)
														return is_done


class 		lowerCamelCase  (a_     ):







							"""simple docstring"""




							def __init__(   self      :	int,					_UpperCAmelCase      :	int,					_UpperCAmelCase      :	int		) ->					int:





														"""simple docstring"""
														warnings.warn(
														    "The class `MaxNewTokensCriteria` is deprecated. "
														    F'''Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '''
														    "with `max_length = start_length + max_new_tokens` instead.",					UpperCamelCase_,					)
														SCREAMING_SNAKE_CASE__  :     List[str]           =		start_length
														SCREAMING_SNAKE_CASE__  :     Optional[int]           =		max_new_tokens
														SCREAMING_SNAKE_CASE__  :     List[str]           =		start_length + max_new_tokens






							@add_start_docstrings(UpperCamelCase_		)
							def __call__(   self      :	Optional[int],					_UpperCAmelCase      :	torch.LongTensor,					_UpperCAmelCase      :	torch.FloatTensor,					**_UpperCAmelCase      :	int		) ->					List[Any]:





														"""simple docstring"""
														return input_ids.shape[-1] >= self.max_length


class 		lowerCamelCase  (a_     ):







							"""simple docstring"""




							def __init__(   self      :	Tuple,					_UpperCAmelCase      :	float,					_UpperCAmelCase      :	Optional[float] = None		) ->					Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__  :     Tuple           =		max_time
														SCREAMING_SNAKE_CASE__  :     Any           =		time.time() if initial_timestamp is None else initial_timestamp






							@add_start_docstrings(UpperCamelCase_		)
							def __call__(   self      :	Any,					_UpperCAmelCase      :	torch.LongTensor,					_UpperCAmelCase      :	torch.FloatTensor,					**_UpperCAmelCase      :	Dict		) ->					Dict:





														"""simple docstring"""
														return time.time() - self.initial_timestamp > self.max_time


class 		lowerCamelCase  (a_     ):







							"""simple docstring"""




							@add_start_docstrings(UpperCamelCase_		)
							def __call__(   self      :	List[Any],					_UpperCAmelCase      :	torch.LongTensor,					_UpperCAmelCase      :	torch.FloatTensor,					**_UpperCAmelCase      :	Union[str, Any]		) ->					Tuple:





														"""simple docstring"""
														return any(criteria(UpperCamelCase_,					UpperCamelCase_		) for criteria in self		)






							@property
							def    A_      (   self      :	Dict		) ->					Union[str, Any]:





														"""simple docstring"""

														for stopping_criterium in self:
																					if isinstance(UpperCamelCase_,					UpperCamelCase_		):
																												return stopping_criterium.max_length
																					elif isinstance(UpperCamelCase_,					UpperCamelCase_		):
																												return stopping_criterium.max_length
														return None




def _a   (    SCREAMING_SNAKE_CASE__     :    StoppingCriteriaList					, SCREAMING_SNAKE_CASE__     :    int  )			->							StoppingCriteriaList:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__  :     List[Any]           =		stopping_criteria.max_length
							SCREAMING_SNAKE_CASE__  :     List[Any]           =		deepcopy(__snake_case  )
							if stopping_max_length is not None and stopping_max_length != max_length:
														warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"					, __snake_case  )
							elif stopping_max_length is None:
														new_stopping_criteria.append(MaxLengthCriteria(max_length=__snake_case  )  )
							return new_stopping_criteria














"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_big_bird import BigBirdTokenizer
else:
 snake_case : Tuple     		=		None

snake_case : Tuple     		=		logging.get_logger(__name__)
snake_case : List[Any]     		=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case : Dict     		=		{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case : int     		=		{
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}


snake_case : List[str]     		=		"""▁"""



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = BigBirdTokenizer
    __UpperCAmelCase           = ["""input_ids""", """attention_mask"""]
    __UpperCAmelCase           = []







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	int=None				,    UpperCamelCase_ :	Dict=None				,    UpperCamelCase_ :	int="<unk>"				,    UpperCamelCase_ :	Tuple="<s>"				,    UpperCamelCase_ :	int="</s>"				,    UpperCamelCase_ :	Union[str, Any]="<pad>"				,    UpperCamelCase_ :	Optional[Any]="[SEP]"				,    UpperCamelCase_ :	int="[MASK]"				,    UpperCamelCase_ :	Tuple="[CLS]"				,    **UpperCamelCase_ :	Optional[Any]				,    ):





           '''simple docstring'''

           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else bos_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else eos_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else unk_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else pad_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else cls_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else mask_token

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    bos_token=UpperCamelCase_				,    eos_token=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    vocab_file
           __magic_name__      				=    False if not self.vocab_file else True







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None				,    UpperCamelCase_ :	bool = False ):





           '''simple docstring'''

           if already_has_special_tokens:
                  if token_ids_a is not None:
                         raise ValueError(
                             'You should not supply a second sequence if the provided sequence of '
                             'ids is already formatted with special tokens for the model.' )
                  return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is None:
                  return [1] + ([0] * len(UpperCamelCase_ )) + [1]
           return [1] + ([0] * len(UpperCamelCase_ )) + [1] + ([0] * len(UpperCamelCase_ )) + [1]







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                      'tokenizer.' )

           if not os.path.isdir(UpperCamelCase_ ):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
                  return
           __magic_name__      				=    os.path.join(
               UpperCamelCase_				,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

           if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase_ ):
                  copyfile(self.vocab_file				,    UpperCamelCase_ )

           return (out_vocab_file,)